



'''simple docstring'''


from abc import ABC, abstractmethod
from typing import Optional, Union

from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike




class   __UpperCamelCase							(_UpperCAmelCase      ):







	def __init__(     self			,					_lowerCAmelCase = None			,					_lowerCAmelCase = None			,					_lowerCAmelCase = None			,					_lowerCAmelCase = None			,					_lowerCAmelCase = False			,					_lowerCAmelCase = False			,					_lowerCAmelCase = None			,					**_lowerCAmelCase			,					)     ->     List[Any]:
							'''simple docstring'''






							lowercase   =				path_or_paths
							lowercase   =				split if split or isinstance(_lowerCAmelCase			,					_lowerCAmelCase    ) else 'train'
							lowercase   =				features
							lowercase   =				cache_dir
							lowercase   =				keep_in_memory
							lowercase   =				streaming
							lowercase   =				num_proc
							lowercase   =				kwargs







	@abstractmethod
	def 	_a (     self    )     ->     Union[Dataset, DatasetDict, IterableDataset, IterableDatasetDict]:
							'''simple docstring'''






							pass




class   __UpperCamelCase							(_UpperCAmelCase      ):







	def __init__(     self			,					_lowerCAmelCase = None			,					_lowerCAmelCase = None			,					_lowerCAmelCase = False			,					_lowerCAmelCase = False			,					_lowerCAmelCase = None			,					**_lowerCAmelCase			,					)     ->     Union[str, Any]:
							'''simple docstring'''






							lowercase   =				features
							lowercase   =				cache_dir
							lowercase   =				keep_in_memory
							lowercase   =				streaming
							lowercase   =				num_proc
							lowercase   =				kwargs







	@abstractmethod
	def 	_a (     self    )     ->     Union[Dataset, IterableDataset]:
							'''simple docstring'''






							pass










"""simple docstring"""


import darl  # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline


__SCREAMING_SNAKE_CASE   =   {
    'n_samples': 64,
    'horizon': 32,
    'num_inference_steps': 20,
    'n_guide_steps': 2,  # can set to 0 for faster sampling, does not use value network
    'scale_grad_by_std': True,
    'scale': 0.1,
    'eta': 0.0,
    't_grad_cutoff': 2,
    'device': 'cpu',
}


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE   =   'hopper-medium-v2'
		__SCREAMING_SNAKE_CASE   =   gym.make(env_name)

		__SCREAMING_SNAKE_CASE   =   ValueGuidedRLPipeline.from_pretrained(
		    'bglick13/hopper-medium-v2-value-function-hor32',
		    env=env,
		)

		env.seed(0)
		__SCREAMING_SNAKE_CASE   =   env.reset()
		__SCREAMING_SNAKE_CASE   =   0
		__SCREAMING_SNAKE_CASE   =   0
		__SCREAMING_SNAKE_CASE   =   10_00
		__SCREAMING_SNAKE_CASE   =   [obs.copy()]
		try:
				for t in tqdm.tqdm(range(T)):
						# call the policy
						__SCREAMING_SNAKE_CASE   =   pipeline(obs, planning_horizon=32)

						# execute action in environment
						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE   =   env.step(denorm_actions)
						__SCREAMING_SNAKE_CASE   =   env.get_normalized_score(total_reward)

						# update return
						total_reward += reward
						total_score += score
						print(
						    F"""Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:"""
						    F""" {total_score}"""
						)

						# save observations for rendering
						rollout.append(next_observation.copy())

						__SCREAMING_SNAKE_CASE   =   next_observation
		except KeyboardInterrupt:
				pass

		print(F"""Total reward: {total_reward}""")